MULTIVARIATE ADAPTIVE REGRESSION SPLINE METHOD AND ITS APPLICATIONS

• Shimokawa Toshio

  Graduate School of Medicine and Engineering, University of Yamanashi

• Tsuji Mitsuhiro

  Faculty of Informatics, Kansai University

• Goto Masashi

  Biostatistical Research Association, NPO

Bibliographic Information

Other Title

• 順序カテゴリカル応答に対する多変量適応型回帰スプライン法
• ジュンジョ カテゴリカル オウトウ ニ タイスル タヘンリョウ テキオウガタ カイキ スプラインホウ

Abstract

In medical research, ordered categorical outcomes (such as seriousness, side effects, and grade of treatment) are sometimes used as response variables. Typically, the influencing factors are explored by ordered logistic regression. Recently, the tree-structured method has been extended to ordered categorical outcomes (Piccarreta, 2008; Archer, 2010), but the predictive outcomes of this approach are poor. In this paper, we newly develop a nonlinear ordered categorical regression method, named PO-MARS, which extends multivariate adaptive regression splines (Friedman, 1991). The PO-MARS method is developed on a proportional odds model framework, and model selection is based on the modified Akaike's information criteria (AIC) proposed by LeBlanc and Crowley (1999). The effectiveness of the PO-MARS method was illustrated through a practical example. In small-scale simulations, this method demonstrated higher predictive performance than existing methods.

Journal

• Bulletin of the Computational Statistics of Japan

  Bulletin of the Computational Statistics of Japan 28 (2), 121-136, 2015

  Japanese Society of Computational Statistics

Keywords

• Ordered categorical data
• Proportional odds model
• Multivariate adaptive regression splnes

Details

• CRID: 1390001204381129728
• NII Article ID: 130005631736
• NII Book ID: AN10195854
• DOI: 10.20551/jscswabun.28.2_121
• ISSN: 21899789
• ISSN: 09148930
• NDL BIB ID: 027073711
• Web Site: https://ndlsearch.ndl.go.jp/books/R000000004-I027073711
• Text Lang: ja
• Data Source:
  • JaLC
  • NDL
  • CiNii Articles
  • KAKEN
• Abstract License Flag: Disallowed